Methods and systems for determining media value

ABSTRACT

Exemplary embodiments are directed to determining a media value associated mentions of an entity in one or more documents based on a sentiment attributed to the mentions of the entity and/or a frequency with which the entity is mentioned. Exemplary embodiments can include a media value engine that can identify mentions of an entity in documents, attribute sentiment to the mentions of the entity; determine a polarity of the sentiment, and calculate a media value attributed to the entity based on the sentiment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/313,342, filed Mar. 12, 2010, the contents of which are herebyincorporated by reference in their entirety.

BACKGROUND

1. Technical Field

Embodiments disclosed herein are directed to determining media valueassociated with entities of interest mentioned in one or more documents.

2.Brief Discussion of Related Art

Typically entities, such as corporations, are willing to pay a fee toadvertise to gain exposure to a target recipient. For example, entitiesmay pay to include an advertisement in a magazine, newspaper, webpage,and the like. Today, entities are being mentioned across the Internet,in news, blogs, tweets, and other social media. This “buzz” can becreated by product launches, ad campaigns, PR events, earnings reports,a single consumer's product experience, and many other triggers, evenscandals. Many times, this buzz is unsolicited by the entity and/oroccurs without requiring the entity to pay a fee. For example, a productmanufactured by an entity can be included in a product review article,an article can discuss financial statements of the entity, and the like.Such mentions or occurrences can have advertising or marketing value.For example, if a product review is negative, the value of the productreview to the entity may be negative or in some instances may beconsidered positive. Likewise, if the product review is positive, thevalue of the product review to the entity may be positive. Taking thisvalue into account can aid in optimizing marketing strategies.

As such, it would be desirable to attribute a media value to thementions or occurrences of entities in documents based on whether thementions or occurrences reflect negative or positive sentiment.

SUMMARY

In one aspect, a method of determining media value of an entity ofinterest is disclosed. The method includes calculating a media valuebased on a frequency of instances of the entity included in the one ormore computer documents.

In another aspect, a non-transitory computer readable medium storinginstructions executable by a computing system including at least onecomputing device is disclosed. Execution of the instructions implementsa method for determining media value of an entity of interest thatincludes calculating a media value based on the sentiment associatedwith the instances of the entity of interest included in the one or morecomputer documents.

In yet another aspect, a system for determining media value of an entityof interest is disclosed. The system can include a computing systemhaving one or more computers. The computing system is configured tocalculate a media value based on the sentiment associated with theinstances of the entity of interest included in the one or more computerdocuments.

In still another aspect, a method of determining media value of anentity of interest is disclosed. The method includes identifyingmentions of an entity in one or more documents, attributing a sentimentto the mentions, determining a polarity of the sentiment, the polaritybeing negative or positive, and calculating a media value based on thesentiment attributed to the entity included in the one or more computerdocuments.

Other objects and features will become apparent from the followingdetailed description considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the drawings aredesigned as an illustration only.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram illustrating an exemplary embodiment of amedia value (MV) engine.

FIG. 2 depicts an exemplary computing device for implementingembodiments of the MV engine.

FIG. 3 depicts an exemplary computing system for implementingembodiments of the MV engine in a networked environment.

FIG. 4 is a flowchart illustrating a process of determining media valuefor entities of interest and generating a media value reporting usingthe media value.

FIG. 5 is a flowchart illustrating calculating media value for an entitybased on sentiment attributed to the entity in one or more documents.

FIG. 6 is an exemplary document that has been marked-up by an embodimentof the MV engine.

FIG. 7 is an exemplary graph that can be generated for outputting amedia value generated by embodiments of the MV engine.

DETAILED DESCRIPTION

Exemplary embodiments are directed to determining media valuecorresponding to an entity based on a frequency with which the entity ismentioned in one or more documents and/or based on sentiment attributedto the entity in one or more documents. Embodiments can scour andanalyze sources of content ‘listening’ in real-time to the mentions ofbrands, products, politicians, celebrities, companies, and the like, andcan calculate media value based on these mentions.

Exemplary embodiments can include a media value engine, which canprocess the one or more documents to identify entities of interest,determine sentiment associated with occurrences of the identifiedentities in the documents, determine a polarity associated with thesentiment, and calculate a media value for the identified entitiesusing, at least in part, the frequency with which the entity ismentioned in the documents, the sentiment, and/or a polarity of thesentiment identified in the documents. Embodiments of the media valueengine can generate an output for an entity, such as a media valuereport, a dashboard display in a web-based user interface, or othersuitable output, which includes the calculated media value for theentity.

As used herein, “media value” refers to an economic value or financialvalue, such as an amount of currency including, for example, dollars,Euros, pounds, Yen, and the like, attributed to the mentions oroccurrences of an entity in one or more documents. As one example, mediavalue can represent the advertising purchase-equivalent value of anentity's media exposure across the web. Using a frequency with which anentity is mentioned and/or a sentiment associated with the mentions asweighting metrics, embodiments of the media value engine can estimatewhat it would have cost to attract the same media exposure throughtraditional advertising channels.

FIG. 1 depicts a block diagram of a media value (MV) engine 100. The MVengine 100 collects a corpus of computer documents and identifiesmentions (e.g., occurrences) of entities in the corpus. An entity can bea name for a given person, place, or thing. For example, an entity canbe a name of a person, a company, a consumer brand, a product, aservice, a university, a city, a state, a country, and the like. Acorpus can refer to classes of document sources from which documents canbe collected. A corpus can include one or more document sources, such associal media postings in a social networking site, microbloggingwebsites, news media articles published by news media websites, pressreleases published by entities on their website or through otherchannels, and the like. Each document source can include one or moredocuments. Mentions or occurrences of an entity in a document can referto instances of the entity included in the document, such as the name ofthe entity, where each instance of the entity in the document is amention or occurrence of the entity in the document.

The MV engine 100 identifies sentiment and a polarity of the sentimentexpressed in the documents and can determine a number of people to whomthe document is distributed or exposed. Sentiment can refer to amanifestation of an opinion, fact, emotion, attitude, bias, and thelike, in a document, which may solicit an interpretation, reaction,response, and the like, from a viewer/observer of the sentiment. Thesentiment can have an associated polarity that can be indicative of thelikely or anticipated interpretation, reaction, response, and the like,of a viewer of the sentiment. For example, sentiment can have a positivepolarity indicating that the sentiment is favorable for the entityassociated with the sentiment, a negative polarity indicating that thesentiment is unfavorable for the entity associated with the sentiment,or a neutral polarity indicating that the sentiment is not positive ornegative. A value can be assigned to the sentiment based on whether thepolarity of the sentiment is positive or negative. In some embodiments,the value assigned to the sentiment can be weighted to give more or lessvalue to sentiment based on a degree of the polarity. In some instances,while the polarity of the sentiment can be negative, the negativesentiment can have a positive, neutral, or negative effect on the mediavalue. Thus, the MV engine 100 can be configured based on the notionthat any exposure is valuable exposure. For example, an entity mayconsider any mention, negative or positive, as having some positivemedia value.

For each entity identified in the corpus of documents, the MV engine 100stores an amount of sentiment expressed, a polarity for the sentiment, adate of publication for each document, an amount of exposure of adocument source from which the documents were obtained, and the like.The MV engine 100 can maintain a media value (MV) database in which thesentiment that has been attributed to one or more entities is stored foreach document source in the corpus and each day that has been processed.The media value for an entity identified in the documents can becalculated based on a total sentiment expressed towards the entity fromeach source in the corpus and/or a frequency with which the entity ismentioned in the documents (e.g., a number of times the entity ismentioned in a document), as well as a total exposure of each source.Media value can refer to an economic value or financial value, such asan amount of currency including, for example, dollars, Euros, pounds,Yen, and the like, attributed to the mentions or occurrences of theentity in the document sources. The media value can be expressed as acumulative value for the documents in the corpus, per document source,per document, and like. The MV engine 100 includes an entity identifier110, a sentiment analyzer 120, a media value (MV) calculator 130, and anoutput generator 140.

The entity identifier 110 identifies mentions, occurrences, or instancesof entities in a document. An entity can be a member of a category ofinterest (e.g. “John Smith” is a member of the category of interest“person”; “General Sentiment” is a member of the category of interest“corporation”). Categories of interest can include people, geographiclocations, consumer brands , products, services, companies,universities, and the like. The entity identifier 110 can receive adocument from the corpus of documents as an input and can produce anoutput identifying occurrences of entities that are found in thedocument. The output of the entity identifier can be a marked-up versionof the document in which the occurrences of the entity can behighlighted using tagging, changes in the color of the text, changes inthe size of the text, changes in the font of the text, and the like. Theentity identifier 110 can include a part-of-speech tagger 112, anatural-language rules analyzer 114 (hereinafter “rules analyzer 114”),and a white-list applier 116.

The part-of-speech tagger 112 can identify a part-of-speech for thewords in the document received by the entity identifier 110. Forexample, based on historical usage patterns (e.g., “dog” is usually anoun, while “fast” can be a noun, verb, or adjective) and commonpatterns of part-of-speech usage, the part-of-speech tagger 112 outputsa part-of-speech for each word in the document. The part-of-speechtagger 112 can generate a marked-up version of the document in which thepart-of-speech tagger 112 can append the part-of-speech to the end ofeach word in the document. The part-of-speech can be appended to eachword as a tag, such as a mark-up language tag.

Once the part-of-speech for the words in the document have beendetermined, the rules analyzer 114 can group words of a documenttogether to identify entities based on a set of pre-determined patterns.The rules analyzer 114 can include a set of rules that can be used bythe rules analyzer 114 to identify entities having a name composed ofmore than one word. The set of rules can be based on parts-of-speechidentified by the parts-of-speech tagger 112. As one example, the rulesanalyzer can include a rule such that when the word “University” appearsin a document, followed by the words “of” or “at”, followed by asequence of proper nouns, such as “Southern California”, the rulesanalyzer combines the words as a single entity (e.g., University ofSouthern California) and identifies the words as a mention or occurrenceof the single entity. The rules analyzer 114 applies the rules to eachsentence in the document to identify entities. The rules analyzer 114can generate a marked-up the version of the document received from thepart-of-speech tagger 112. The occurrences of the entity identifiedusing the rules analyzer 114 can be highlighted using tagging, changesin the color of the text, changes in the size of the text, changes inthe font of the text, and the like.

The white-list applier 116 of the entity identifier 110 can facilitateautomatic recognition of entities in the documents. The white-listapplier 116 can include a set of words and/or phrases representing thenames of entities to be automatically recognized in the documents. Thewhite-list applier 116 can ensure that occurrences of specific entitiesin documents are identified and can facilitate identification ofentities included in the list without requiring the part-of-speechtagger 112 and/or the rules analyzer 114 to detect the entities. Thus,the white-list applier can be used in combination with thepart-of-speech tagger 112 and the rules analyzer 114 to identify some orall of the entities mentioned in the documents. The white-list applier116 can scan the document for instances of the entries in the list andcan compare the words and/or phrases in the list to the words and/orphrases in the document, and when a word or phrase in the documentmatches a word and/or phrase in the list, the white-list applier 116 canidentify the word and/or phrase in the document as a name of an entity.The white-list applier 116 can generate a marked-up version of thedocument, or can mark-up the version of the document output by thepart-of-speech tagger 112 and/or rules analyzer 114, in which theentities identified by the white-list applier 116 can be can behighlighted using tagging, changes in the color of the text, changes inthe size of the text, changes in the font of the text, and the like.

The sentiment analyzer 120 can identify sentiment expressed in adocument, a polarity of the sentiment, and entities to which thesentiment is directed. The sentiment analyzer 120 can use naturallanguage processing to identify the sentiment expressed in a documentand can determine an amount of sentiment attributed to each entityidentified in a document. For example, the sentiment analyzer 120 canidentify a cumulative amount of sentiment having a positive polarity anda negative polarity in a document. The sentiment analyzer 120 canreceive the marked-up version of the document output by the entityidentifier 110 as an input and can output the sentiment expressedtowards each entity identified in the document. The sentiment analyzer120 includes a sentiment lexicon generator 122, a sentiment wordidentifier 124, a sentiment attribution analyzer 126, and a sentimentaggregator 128. Those skilled in the all will recognize that sentimentin a document can be identified using other techniques and thatsentiment identification is not limited to the illustrative embodimentsdescribed herein.

The sentiment lexicon generator 122 can generate a lexicon of sentimentwords and/or phrases using a computer dictionary of synonym/antonymrelationships between words and/or phrases. In some embodiments, a smallseed set of positive and negative sentiment words can be used to derivethe lexicon of sentiment words. In some embodiments, sentiment lexicongeneration by the sentiment lexicon generator 122 can use path analysis.Expanding seed lists into lexicons can be performed using recursivequerying for synonyms using a computer dictionary. The sentiment lexicongenerator can expand a set of seed words using synonym and antonymqueries. A polarity (positive or negative) can be associated with thewords and/or phrases in the sentiment lexicon and synonyms and antonymsof the words and/or phrases can be identified. Synonyms of a word and/orphrase inherit the polarity from the parent, whereas antonyms of theword and/or phrase inherit the opposite polarity.

The sentiment word identifier 124 receives the document from the entityidentifier 110 and the sentiment lexicon generated by a sentimentlexicon generator 122 as an input and outputs the identified sentimentwords and/or phrases based on the sentiment lexicon along with anyassociated modifiers, such as, for example, “not”, “very”, and the like,which can modify the sentiment (e.g. “not” reverses polarity, “very”magnifies sentiment). For example, the sentiment word identifier 124 cancompare words and/or phrases in the sentiment lexicon to words and/orphrases in the document. The words indicating sentiment can beidentified by marking-up the document.

The sentiment attribution analyzer 126 receives the document with theidentified entities and sentiment in the marked-up document andattributes the identified sentiment to the entities. In someembodiments, the sentiment attribution analyzer 126 attributes sentimentin a sentence to all entities identified in the sentence. In someembodiments, sentiment can be attributed to an instance of an entityoccurring closest to the sentiment (e.g., the entity with the leastnumber of words between the sentiment and the instance of the entity).

The sentiment aggregator 128 enters an entry in the MV databaserepresenting an amount of sentiment towards entities encountered in thedocument, along with a date the document was published and the sourcethat published the document. In some embodiments, the sentimentaggregator 128 can sum the number of positive sentiment words attributedto an entity in the document and can subtract the number of negativesentiment words attributed to the entity in the document from the sum.In some embodiments, negative sentiment words that have a negativeeffect on the media value can be subtracted from the sum of the numberof positive sentiment words. In some embodiments, negative sentimentwords that have a positive effect on the media value can be added to thesum of the positive sentiment words.

The MV calculator 130 calculates media value associated with exposure ofan entity based on results of the entity identifier 110 and sentimentAnalyzer 120 including, but not limited to occurrences of the entity inthe document, a sentiment (and polarity of the sentiment) attributed tothe occurrences, an amount of exposure or distribution the documentshave, and the like. The MV calculator 130 can query the MV database forthe results of entity identification and sentiment analysis. Using this,the MV calculator 130 can produce a total or cumulative media value forthe entity and can calculate the media value associated with the corpusof documents, each of the document sources, each of the documents, andthe like. The MV calculator 130 can include an exposure weighting unit132 and a calculation unit 134.

The exposure weighting unit 132 can determine a number of people towhich a document has been distributed or exposed. A document isdistributed or exposed to a person when it is e-mailed to the person,tweeted to the person, accessed by a person via a browser, downloaded,or otherwise made available to the person. Distribution or exposure ofclasses of sources can be measured in different ways to determine theamount of people to which a particular document from that source isexposed. For example, traditional news media sources measure physicalcirculation; web-based sources can be measured using a number of hits awebsite receives, an Alexa rank, and the like; and micro-blog sources,such as Twitter, can be measured by the number of followers a sourcehas. The exposure weighting unit 132, examines one or more of thesetypes of measures and produces as an output, for each particulardocument in a class of sources, an approximation of the number of peopleto which a document of a particular source is distributed or exposed.

To generate the media value associated with a specified entity, during aspecified time period, in a specified corpus of documents, the number ofmentions, the sentiment polarity of those mentions, and the exposureweighting of those sources, during the specified time period, in thespecified corpus, can be extracted from the MV database. For each sourcein the corpus, the media value for the specified entity, in thespecified date range is calculated according to the followingmathematical expression:

mediavalue=((rw*ref(entity))+(nw*neg_ref(entity))+(pw*pos_ref(entity)))*(exposure(source)*dollars/eyeball),

where:rw refers to the weight assigned by a user to an entity identified inthe corpus of documents;nw refers to the weight assigned by the user to a negative polarity forthe entity identified in the corpus;pw refers to the weight assigned by the user to a positive polarityentity identified in the corpus;ref(entity) refers to a total number of references to the specifiedentity in the given source during the given date range, which isextracted from the database and calculated in the sentiment analyzer;neg ref(entity) refers to a total number of references with negativesentiment polarity to the specified entity in the given source duringthe given date range, which is extracted from the database andcalculated in the sentiment analyzer;pos_ref(entity) refers to a total number of references with positivesentiment polarity to the specified entity in the given source duringthe given date range, which is extracted from the database andcalculated in the sentiment analyzer;exposure(source) refers to the number of people that a documentpublished in the given source is exposed to, which is extracted from thedatabase, and calculated in the exposure weighting unit;dollars/eyeball refers to the amount of money, specified by the user,that the user values for the specified entity being exposed to oneperson, from the specified corpus;the expression rw*ref(entity) can be referred to as a weighted referencevalue;the expression nw*neg ref(entity) can be referred to as a negativeentity reference value;the expression pw*pos_ref(entity) can be referred to as a positiveentity reference value; andthe expression exposure(source)*dollars/eyeball can be referred to as amedia value multiplier.

The cost of advertisements can be a function of the number of readers onthe given media channel (a specific newspaper, website, blog, etc.),which can be expressed in terms of “cost per thousand” or similarquantities. The dollars/eyeball value can be determined based onpublished and estimated advertising rates for published or onlineadvertisements (e.g. per thousand hits/impressions). Thus, differentdocument sources may have different advertising rates.

The output generator 140 generates and outputs media value to users. Forexample, the output generator 140 can include a media value reportgenerator 142 that generates media value reports (MVRs) based on theresult of media value calculations, in response to user queries, a userinterface or dashboard 144 that can be accessed by a user to view mediavalue as well as other information attributed to one or more entities,and/or one or more application program interfaces (APIs) 146 that allowsusers to interface with the output generator using one or moreapplications. The output generator 140 takes as input the desiredentities and time frame from the user and outputs media value attributedto the desired entities. The time frame can be specified as a range ofdates, such as Oct. 15, 2009 to Jan. 10, 2010, or can be specifiedrelative to the current date, such as yesterday, last week, last year,month-to-date, year-to-date, and the like. In some embodiments, theoutput can contain a total amount of media value for the entity duringthe specified time range; a time series showing the amount of mediavalue for each day during the specified time range; a list of sourcesthat contributed to the media value for the specified entity, during thespecified time range, ordered by the amount of media value contributed;and the like.

FIG. 2 depicts an exemplary computing device 200 for determining mediavalue for entities of interest using the MV engine 100. The computingdevice 200 can be a mainframe; personal computer (PC); laptop computer;workstation; handheld device, such as a PDA and/or smart phone; and thelike. In the illustrated embodiment, the computing device 200 includes acentral processing unit (CPU) 202 and can include storage 204 forstoring data and instructions. The storage 204 can include computerreadable medium technologies, such as a floppy drive, hard drive,compact disc, tape drive, Flash drive, optical drive, read only memory(ROM), random access memory (RAM), and the like. The computing devicecan include a display device 206 that enables the computing device 200to communicate with user through a visual display and can include dataentry device(s) 208, such as a keyboard, touch screen, microphone,and/or mouse.

Applications 210, including the MV engine 100, can be resident in thestorage 204. The applications 210 can include instructions forimplementing the MV engine 100. The instructions can be implementedusing, for example, C, C—H—, Java, JavaScript, Basic, Pert, Python,assembly language, machine code, and the like. The storage 204 can belocal or remote to the computing device 200. The computing device 200includes a network interface 212 for communicating with a network. TheCPU 202 operates to run the applications 210 in storage 204 by executinginstructions therein and storing data resulting from the performedinstructions, which may be output via a display 206 or by othermechanisms known to those skilled in the art, such a print out from aprinter.

FIG. 3 is an exemplary networked computing system 300 for implementingembodiments of the MV engine. The computing system 300 includes one ormore servers 310 and 320 coupled to clients 330 and 340, via acommunication network 350, which can be any network over whichinformation can be transmitted between devices communicatively coupledto the network. The system 300 can also include repositories or databasedevices 360, which can be coupled to the servers 310/320 and clients330/340 via the communications network 350. The servers 310/320, clients330/340, and database devices 360 can be implemented using a computingdevice.

The servers 310/320, clients 330/340, and/or databases 360 can storeinformation, such as sentiment attributed to one or more entitiesmentioned in a corpus of documents; media value associated with one ormore entities mentioned in the corpus of documents; a list of entitiesto be automatically identified in the corpus of documents; a sentimentlexicon; and the like. In some embodiments, the MV engine 100 can bedistributed among the servers 310/320, clients 330/340, and databasedevices 360 such that one or more components of the MV engine 100 and/orportion of one or more components of the MV engine 100 can beimplemented by a different device (e.g. clients, servers, databases) inthe communication network 350. For example, in some embodiments, theentity identifier 110 and the sentiment analyzer can be resident on theserver 310, the MV calculator 130 can be resident on the server 320, theoutput generator 140 can be resident on the clients 330 and 340. One ormore of the databases 360 can serve of the MV database to store entityinformation, sentiment and polarity information, media valueinformation, media value reports, a corpus of documents, and the like.Those skilled in the art will recognize that the distribution ofcomponents of the MV engine is illustrative and that differentdistributions of the components of the MV engine can be implemented.

FIG. 4 is an exemplary flowchart illustrating an exemplary process fordetermining media value and generating an media value output. To begin,the MV engine can collect documents for one or more document sources(400). Documents are collected in a manner that is appropriate for theirdomain. For example, Twitter documents are collected using Twitter'spublished application program interfaces (API's) and newspaper articlesare collected by programs that download and clean-up webpages from thenewspapers website. Once the MV engine has collected the documents, theMV engine performs entity identification on the documents (402) andperforms sentiment analysis of the documents (404). EntityIdentification identifies occurrences of entities of interest(names ofpeople, companies, consumer brands, etc.) in the documents. Sentimentanalysis identifies the expression of opinion and the polarity of thatopinion in the documents and attributes that sentiment to the identifiedentities.

The MV engine looks at each publication source in the corpus, anddetermines, based on source specific information, the number of peoplethat a document published by that source is exposed (406). For example,for a newspaper article, the circulation of the newspaper in which thearticle was published is approximately the number of people to which thearticle was exposed. The MV engine takes the exposure of the documentssources and the attributed sentiment of the documents and calculates themedia value attributed to the entity in each document source (408). Themedia value for each document source, on each day in a specified timeframe can be used to produce an output, such as a MVR or a display on adashboard (e.g., a web-based user interface), containing time series ofmedia value over the time period, document sources ranked by valuecreated, a total amount of media value generated by the entity over thedate range, and the like (410).

FIG. 5 is a flowchart illustrating an exemplary calculation of the mediavalue for an entity based on sentiment attributed to the entity in oneor more documents. The MV engine can determine a weighted valueattributed to the entity (500) and can determine a total number ofmentions or occurrences of the entity in one or more of the documents(502). The weighted value attributed to the entity can be multiplied bythe total number of references to the entity in one or more of thedocuments to generate a weighted entity reference value (504).

The MV engine can determine a weighted value attributed to a negativepolarity for the entity (506) and can determine a total number ofreferences to the entity having a negative sentiment polarity (508). Theweighted value attributed to the negative polarity can be multiplied bythe total number of references to the entity having the negativesentiment polarity to generate a negative entity reference value (510).The total number of mentions or occurrences of the entity that have anegative sentiment polarity can be determined with respect to aspecified document source during a specified date of publication range.

The MV engine can determine a weighted value attributed to a positivepolarity for instances of the entity in the one or more documents (512)and can determine a total number of references to the entity having apositive sentiment polarity. The weighted value attributed to thepositive polarity can be multiplied by the total number of references tothe entity having the positive sentiment polarity to generate a positiveentity reference value (514). The total number of mentions oroccurrences of the entity that have a positive sentiment polarity can bedetermined with respect to a specified source during a specified date ofpublication range.

The MV engine can determine an exposure number representing a number ofpeople to which the one or more documents are distributed (516) and candetermine an economic value attributed to exposure of the one or moredocuments to one person (518). The exposure number can be multiplied bythe economic value to generate a media value multiplier (520).

A sentiment activity sum can be calculated by adding the weighted entityreference value, the negative entity reference value, and the positiveentity reference value (522). The media value attributed to mentions oroccurrences of the entity in one or more documents can be generated bymultiplying the sentiment activity sum by the media value multiplier(524). Those skilled in the art will recognize the order in which thecalculation of the media value is calculated can vary and that theordered described with respect to FIG. 5 is illustrative of an exemplarymedia value calculation process. Further, those skilled in the art willrecognize that the media value can be calculated using varioustechniques and that the techniques described herein are illustrative anexemplary media value calculation process.

FIG. 6 is an exemplary screenshot of a document 600 that has beenmarked-up by the MV engine. The document can include tags foridentifying entities, sentiment attributed to the entities, a polarityof the sentiment attributed to the entities, and the like. For example,the MV engine can identify the entity “Microsoft” as being mentioned inthe document and can associate a sentiment and polarity of the sentimentto the mention of “Microsoft”. The MV engine can also associate theentity with a category of interest and can associate a sentiment valuewith the mention of the entity. For example, the MV engine can classifythe entity “Microsoft” as a corporation.

FIG. 7 is an exemplary graph 700 illustrating media value generated bythe MV engine that can be included in a media value report (MVR), adashboard (e.g., a web-based user interface), or in any other suitableformat. The graph 700 can have a y-axis 710 representing a media valueand an x-axis representing time over which a media value has beengenerated. A plot 730 of the media value versus time can be displayed inthe graph 700. The plot 730 can identify a total media value over timefor a corpus of documents, one or more document sources, specifieddocuments, and the like. In the present example, the plot identifies thecontribution of to the media value from three document sources: newsmedia; social media; and Twitter.

1. A method of determining media value of an entity of interestcomprising: calculating a media value based on a frequency of instancesof the entity included in one or more documents, wherein calculating themedia value is based on a sentiment associated with the instances of theentity included in the one or more documents and wherein calculating themedia value comprises: determining a weighted value attributed to theentity; determining a total number of references to the entity in theone or more documents; and multiplying the weighted value attributed tothe entity by the total number of references to the entity in the one ormore documents to generate a weighted entity reference value. 2-10.(canceled)
 11. The method of claim 1, further comprising: identifyinginstances of the entity included in one or more computer documents; andassociating a sentiment with the instances of the entity.
 12. The methodof claim 1, further comprising: generating a media value report usingthe media value.
 13. The method of claim 1, wherein the media value isrepresented in terms of a financial value. 14-15. (canceled)
 16. Asystem for determining media value of an entity of interest comprising:a computing system having one or more computers, the computing systembeing configured to calculate a media value based on a frequency ofinstances of the entity included in one or more computer documentswherein calculating the media value is based on sentiment associatedwith instances of the entity included in one or more computer documentsand wherein calculating the media value comprises: determining aweighted value attributed to the entity; determining a total number ofreferences to the entity in the one or more computer documents; andmultiplying the weighted value attributed to the entity by the totalnumber of references to the entity in the one or more computer documentsto generate a weighted entity reference value.
 17. The method of claim1, wherein the one or more documents are published within a specifiedtime range.
 18. The method of claim 1, wherein calculating the mediavalue further comprises: determining an exposure number representing anumber of people to which the one or more documents are distributed;determining an economic value per person attributed to exposure of theone or more documents; and multiplying the exposure number by theeconomic value to generate a media value multiplier.
 19. The system ofclaim 16, wherein the one or more computer documents are publishedwithin a specified time range.
 20. The system of claim 16, whereincalculating the media value further comprises: determining an exposurenumber representing a number of people to which the one or more computerdocuments are distributed; determining an economic value per personattributed to exposure of the one or more computer documents; andmultiplying the exposure number by the economic value to generate amedia value multiplier.